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A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network

Author

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  • Yadong Pei

    (Key Laboratory of Public Big Data Security Technology, Chongqing College of Mobile Communication, Chongqing 401420, China
    Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing 401420, China)

  • Chiou-Jye Huang

    (College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China)

  • Yamin Shen

    (School of Information Science and Technology, Donghua University, Shanghai 201620, China)

  • Mingyue Wang

    (Key Laboratory of Public Big Data Security Technology, Chongqing College of Mobile Communication, Chongqing 401420, China
    Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing 401420, China)

Abstract

Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot prices has become critical to energy management, economic growth, and environmental protection. This work offers a novel model based on the temporal convolutional network (TCN) and dynamic learning rate for predicting natural gas spot prices over the following two weekdays. The residual block structure of TCN provides good prediction accuracy, and the dilated causal convolutions minimize the amount of computation. The dynamic learning rate setting was adopted to enhance the model’s prediction accuracy and robustness. Compared with three existing models, i.e., the one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), and long short-term memory (LSTM), the proposed model can achieve better performance over other models with mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) scores of 4.965%, 0.216, and 0.687, respectively. These attractive advantages make the proposed model a promising candidate for long-term stability in natural gas spot price forecasting.

Suggested Citation

  • Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2321-:d:1083454
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    References listed on IDEAS

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    1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    2. Gürsan, C. & de Gooyert, V., 2021. "The systemic impact of a transition fuel: Does natural gas help or hinder the energy transition?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    4. Hamid Abrishami & Vida Varahrami, 2011. "Different methods for gas price forecasting," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(96), pages 137-144, Diciembre.
    5. Apostolos Serletis & Asghar Shahmoradi, 2007. "Returns and Volatility in the NYMEX Henry Hub Natural Gas Futures Market," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 15, pages 193-204, World Scientific Publishing Co. Pte. Ltd..
    6. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    7. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    8. Buchanan, W. K. & Hodges, P. & Theis, J., 2001. "Which way the natural gas price: an attempt to predict the direction of natural gas spot price movements using trader positions," Energy Economics, Elsevier, vol. 23(3), pages 279-293, May.
    9. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    10. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
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    Cited by:

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